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Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have
emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and
structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations
of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering
scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for
CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier
to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear
dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE).
Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities
following image registration. However multimodal data integration is non-trivial when the individual
modalities include spectral and image intensity data. We propose a data combination solution wherein we project
the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional
embedding space via NLDR. NLDR methods preserve the relationships between the objects in the
original high dimensional space when projecting them into the reduced low dimensional space. Since the original
spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional
space, data at the same spatial location can be integrated by concatenating the respective embedding vectors.
Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS
and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets
of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial
extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared
to corresponding CAD results with the individual modalities.
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